scholarly journals Development of a SGLI Adaptable Fire Radiative Power Estimation Algorithm using SWIR Observation Channels

2018 ◽  
Vol 57 (6) ◽  
pp. 277-288
Author(s):  
Takashi MIURA ◽  
Masao MORIYAMA
Keyword(s):  
Estimation Algorithm ◽  
Power Estimation ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals A Bi-Spectral Microbolometer Sensor for Wildfire Measurement

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3690
Author(s):  
Denis Dufour ◽  
Loïc Le Noc ◽  
Bruno Tremblay ◽  
Mathieu N. Tremblay ◽  
Francis Généreux ◽  
...  
Keyword(s):  
Low Resource ◽  
Long Wave ◽  
Radiative Power ◽  
Infrared Radiances ◽  
Moderate Scale ◽  
Long Wave Infrared ◽  
Detector Type ◽  
Experimental Burns ◽  
Fire Radiative Power

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

scholarly journals Real-Time Detection of Daytime and Night-Time Fire Hotspots from Geostationary Satellites

2021 ◽  
Vol 13 (9) ◽  
pp. 1627
Author(s):  
Chermelle B. Engel ◽  
Simon D. Jones ◽  
Karin J. Reinke
Keyword(s):  
Real Time ◽  
Infrared Imaging ◽  
Night Time ◽  
Clear Link ◽  
Radiative Power ◽  
Biogeographical Region ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fire Radiative Power

This paper introduces an enhanced version of the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm. The algorithm runs in real-time and operates over 24 h to include both daytime and night-time detections. The algorithm was executed and tested on 12 months of Himawari-8 data from 1 April 2019 to 31 March 2020, for every valid 10-min observation. The resulting hotspots were compared to those from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The modified BRIGHT hotspots matched with fire detections in VIIRS 96% and MODIS 95% of the time. The number of VIIRS and MODIS hotspots with matches in the coincident modified BRIGHT dataset was lower (at 33% and 46%, respectively). This paper demonstrates a clear link between the number of VIIRS and MODIS hotspots with matches and the minimum fire radiative power considered.

scholarly journals Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions

2017 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS). GFAS converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence where observed FRP from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an overestimation of fire duration, which in turn translates into an overestimation of emissions from fires. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

First retrieval of fire radiative power from COMS data using the mid-infrared radiance method

2016 ◽  
Vol 8 (2) ◽  
pp. 116-125 ◽  
Author(s):  
Daesun Kim ◽  
Jaeil Cho ◽  
Sungwook Hong ◽  
Hanlim Lee ◽  
Myoungsoo Won ◽  
...  
Keyword(s):  
Mid Infrared ◽  
Infrared Radiance ◽  
Radiative Power ◽  
Radiance Method ◽  
Fire Radiative Power

scholarly journals Influence of Satellite Sensor Pixel Size and Overpass Time on Undercounting of Cerrado/Savannah Landscape-Scale Fire Radiative Power (FRP): An Assessment Using the MODIS Airborne Simulator

Fire ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 11
Author(s):  
Samuel Sperling ◽  
Martin J. Wooster ◽  
Bruce D. Malamud
Keyword(s):  
Landscape Scale ◽  
Low Noise ◽  
Pixel Size ◽  
Ir Detectors ◽  
Fire Cycle ◽  
Early Afternoon ◽  
Radiative Power ◽  
Satellite Sensor ◽  
Fire Radiative Power ◽  
Wide Swath

The fire radiative power (FRP) of active fires (AFs) is routinely assessed with spaceborne sensors. MODIS is commonly used, and its 1 km nadir pixel size provides a minimum per-pixel FRP detection limit of ~5–8 MW, leading to undercounting of AF pixels with FRPs of less than around 10 MW. Since most biomes show increasing AF pixel frequencies with decreasing FRP, this results in MODIS failing to detect many fires burning when it overpasses. However, the exact magnitude of the landscape-scale FRP underestimation induced by this type of AF undercounting remains poorly understood, as does its sensitivity to sensor pixel size and overpass time. We investigate these issues using both 1 km spaceborne MODIS data and 50 m MODIS Airborne Simulator (MAS) observations of the Brazilian cerrado, a savannah-like environment covering 2 million km2 (>20%) of Brazil where fires are a frequent occurrence. The MAS data were collected during the 1995 SCAR-B experiment, and are able to be spatially degraded to simulate data from sensors with a wide variety of pixel sizes. We explore multiple versions of these MAS data to deliver recommendations for future satellite sensor design, aiming to discover the most effective sensor characteristics that provide negligible pixel-area related FRP underestimation whilst keeping pixels large enough to deliver relatively wide swath widths. We confirm earlier analyses showing 1 km MODIS-type observations fail to detect a very significant number of active fires, and find the degree of undercounting gets worse away from the early afternoon diurnal fire cycle peak (~ 15:00 local time). However, the effect of these undetected fires on the assessment of total landscape-scale FRP is far less significant, since they are mostly low FRP fires. Using two different approaches we estimate that the MODIS-type 1 km data underestimates landscape scale FRP by around a third, and that whilst the degree of underestimation worsens away from the diurnal fire cycle peak the effect of this maybe less important since there are far fewer fires present. MAS data degraded to a 200 m spatial resolution provides landscape-scale FRP totals almost indistinguishable from those calculated with the original 50 m MAS observations, and still provides a pixel size consistent with a wide swath imaging instrument. Our work provides a potentially useful guide for future mission developers aiming at active fire and FRP applications, and we conclude that such missions need operate at spatial resolutions no higher than 200 m if they rely on cooled, low-noise IR detectors. Further work confirming this for fire-affected biomes beyond the savannah-type environments studied here is recommended.

scholarly journals The Use of Fire Radiative Power to Estimate the Biomass Consumption Coefficient for Temperate Grasslands in the Atlantic Forest Biome

2017 ◽  
Vol 32 (2) ◽  
pp. 255-260 ◽  
Author(s):  
Bibiana Salvador Cabral da Costa ◽  
Eliana Lima da Fonseca
Keyword(s):  
Atlantic Forest ◽  
Field Data ◽  
Consumption Rate ◽  
Radiant Energy ◽  
Important Variable ◽  
Remotely Sensed Data ◽  
Radiative Power ◽  
Consumption Coefficient ◽  
Forest Biome ◽  
Fire Radiative Power

Abstract Every year, many active fire spots are identified in the satellite images of the southern Brazilian grasslands in the Atlantic Forest biome and Pampa biome. Fire Radiative Power (FRP) is a technique that uses remotely sensed data to quantify burned biomass. FRP measures the radiant energy released per time unit by burning vegetation. This study aims to use satellite and field data to estimate the biomass consumption rate and the biomass consumption coefficient for the southern Brazilian grasslands. Three fire points were identified in satellite FRP products. These data were combined with field data, collected through literature review, to calculate the biomass consumption coefficient. The type of vegetation is an important variable in the estimation of the biomass consumption coefficient. The biomass consumption rate was estimated to be 2.237 kg s-1 for the southern Brazilian grasslands in Atlantic Forest biome, and the biomass consumption coefficient was estimated to be 0.242 kg MJ-1.

scholarly journals Estimation of Direct Fire Emissions from Forests Burning in Siberia

2020 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Evgenii I. Ponomarev
Keyword(s):  
Carbon Emissions ◽  
Summer Period ◽  
Central Siberia ◽  
Fire Emissions ◽  
Emission Estimate ◽  
Radiative Power ◽  
Order Of Magnitude ◽  
Siberian Forests ◽  
Fire Radiative Power

Using a database on wildfires recorded by remote sensing for 1996–2020, we assessed the seasonal variation of direct carbon emissions from the burning in Siberian forests. We have implemented an approach that takes into account the combustion parameters and the changing intensity of the fire (in terms of Fire Radiative Power (FRP)), which affects the accuracy of the emission estimate. For the last two decades, the range of direct carbon emissions from wildfires was 20–250 Тg С per year. Sporadic maxima were fixed in 2003 (>150 Тg С/year), in 2012 (>220 Тg С/year), and in 2019 (>190 Тg С/year). Preliminary estimation of emissions for 2020 (on 30th of September) was ~180 Tg С/year. Fires in the larch forests of the flat-mountainous taiga region (Central Siberia) made the greatest contribution (>50%) to the budget of direct fire emission, affecting the quality of the atmosphere in vast territories during the summer period. According to the temperature rising and forest burning trend in Siberia, the fire emissions of carbon may double (220 Тg С/year) or even increase by an order of magnitude (>2000 Тg С/year) at the end of the 21st century, which was evaluated depending on IPCC scenario.

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